Rapid changes in traffic patterns on a communication network, such as traffic concentration around the same time, are occurring with increasing frequency in these years. To address such rapid variations in traffic patterns, routing control techniques based on attractor selection, which models the mechanism of living organisms behaving adaptively to environmental changes are being studied as one of candidate methods for constructing future new generation network infrastructures. Attractor selection is one of approaches that is based on a biological paradigm and models a state in which living organisms create a stable state (attractor).
In classical control methods in general, responses to changes in a given environment are provided beforehand as algorithms for adapting to environmental changes. Accordingly, adaptation to expected environmental changes can be achieved with a high degree of accuracy. On the other hand, adaptation to unexpected environmental changes adds to the complexity of a system and is difficult to implement. To address the problem, methods that model living organisms have been proposed. Attractor selection, which is one of such methods, uses fluctuations to perform control and therefore has accuracy slightly lower than the classical control methods but has the property of more readily adapting to unknown environmental changes. Accordingly, attractor selection control infrastructure techniques, which are communication techniques to which the attractor selection is applied, have the advantage that systems can flexibly respond to environmental changes such as device failures and communication quality variations.
A fluctuation equation is generally written as:
                    [                  Equation          ⁢                                          ⁢          1                ]                                                                                  ⅆ            m                                ⅆ            t                          =                              α            ·                          f              ⁡                              (                m                )                                              +          η                                    (        1        )            
The right-hand side of Equation (1) represents fluctuation information and η represents a fluctuation term (noise term). Attractor selection has two behaviors: the fluctuation term η (noise term) and a control structure f(m) that has an attractor. The behaviors are controlled by an activity α. The activity α is a value indicating a state of the system. When the state of the system degrades, the activity α decreases and the influence of the fluctuation η increases relatively. As a result, the state m of the system randomly changes. When m changes and the state of the system is improved, the activity α increases and m is controlled by f(m). Such a mechanism represents a model in which living organisms respond to environmental changes. Description of typical attractor selection is provided in section III of Non Patent Literature 1.
Patent Literature 1 describes a virtual network control method and a virtual network control device that are capable of following environmental variations by using fluctuation equations.
Non Patent Literature 1 describes a next-generation wireless network capable of following environmental variations by using fluctuation equations.